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Job Description

  • Develop and optimize data processing jobs using PySpark for complex transformations and aggregations, focusing on scalability and performance.
  • Design and implement robust data pipelines on cloud platforms, ensuring high-quality data processing and storage solutions.
  • Utilize cloud services like EC2 and S3 for data warehousing and analytics, leveraging their strengths in scalability and performance.
  • Manage database schema design, query optimization, and performance tuning to achieve optimal results.
  • Design and maintain scalable data architectures using best practices in data modeling and ETL, ensuring efficient data flow.
  • Leverage modern data platforms for collaborative data science across varied sources, fostering innovation and growth.
  • Maintain Spark clusters and Databricks jobs for optimal performance and data quality, ensuring seamless workflows.
  • Implement security measures, backup procedures, and disaster recovery plans using industry best practices, safeguarding critical assets.
  • Manage source code and automate deployment via DevOps tools with CI/CD pipelines, streamlining development and operations.
  • Troubleshoot and optimize PySpark scripts, Databricks notebooks, SQL queries, and Airflow DAGs for efficient data processing.
  • Stay current with advancements in cloud data technologies and recommend useful tools, driving business success.
  • Use Apache Airflow for orchestrating and automating data workflows reliably, ensuring consistent outcomes.
  • Collaborate with data scientists and analysts to build models and pipelines for analytics and ML projects, unlocking business insights.